Optimal Transmission Switching: Improving Exact Algorithms by Parallel Incumbent Solution Generation

Hinneck, Anton; Pozo, David

doi:10.1109/tpwrs.2022.3199114

Cited by 6 publications

(4 citation statements)

References 42 publications

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“…OTS is highlighted as a progressive solution to mitigate losses generated by contingencies while optimizing the efficiency of electrical networks by controlling short circuit currents and overloads. All of this is achieved without incurring additional costs in the [25] operation.…”

Section: Optimal Transmission Line Switching (Ots)mentioning

confidence: 99%

Minimization of Transmission Line Losses through Corrective Control of System Topology

Orbea,

Carrión,

Jaramillo

2024

Preprint

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This research proposes a methodology for minimizing losses in transmission lines (LT), considering the reconfiguration of the architecture of the electrical power system (SEP). The implementation of this methodology redirects the power flow with optimal switching through its LTs to guarantee the stability of the voltage, angle, frequency, and power balance to minimize losses that affect the reliability and quality of the system. Optimal transmission switching (OTS) allows various types of analysis to be carried out, where the loadability of the lines, response times, and operating costs, among others, can be improved. This article proposes to minimize the losses in the transmission lines with the OTS by using AC power flows as a mixed integer nonlinear problem (MINLP). Several test scenarios evaluate the method’s effectiveness, determining the optimal topology for corrective control that optimizes power flows in different situations. It has been proven that this approach reduces losses compared to a base case by 99%, even in the face of N-1 or random contingencies, without losing load, maintaining the same active power dispatch, and finally verifying a strategic increase in dispatch of reactive power to maintain operating parameters within stable limits.

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Section: Optimal Transmission Line Switching (Ots)mentioning

confidence: 99%

Minimization of Transmission Line Losses through Corrective Control of System Topology

Orbea,

Carrión,

Jaramillo

2024

Preprint

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“…There are several parallel computing applications in the power systems area. In [27], the authors solved the optimal switching problem by parallel processing of mixed-integer linear programming. The authors of [28] solved the reactive power optimization problem by using an adaptive differential evolution method.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimum Parallel Processing Schemes to Improve the Computation Speed for Renewable Energy Allocation and Sizing Problems

et al. 2022

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The optimum penetration of distributed generations into the distribution grid provides several technical and economic benefits. However, the computational time required to solve the constrained optimization problems increases with the increasing network scale and may be too long for online implementations. This paper presents a parallel solution of a multi-objective distributed generation (DG) allocation and sizing problem to handle a large number of computations. The aim is to find the optimum number of processors in addition to energy loss and DG cost minimization. The proposed formulation is applied to a 33-bus test system, and the results are compared with themselves and with the base case operating conditions using the optimal values and three popular multi-objective optimization metrics. The results show that comparable solutions with high-efficiency values can be obtained up to a certain number of processors.

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“…[20] where an approximate model of the TS problem is proposed to speed up the computation time and improve solution quality was proposed, recently, the authors in ref. [21] propose an asynchronous algorithmic design that exploits domain‐specific knowledge and heuristics in parallel to speed up the full NP‐hard TS problem. New challenges that arise in uncertainty in variable renewable energy production [22] and in markets [23] have spurred further research into TS.…”

Section: Introductionmentioning

confidence: 99%

“…There, when selecting a supervised ML model for real‐time reliability studies [25], the topological configuration can result in discrete changes in the underlying data distributions that challenge the learned ML models [26]. Hence, exploring these discrete topological changes is an alternative that is then trained to a ML model through reinforcement rather than supervision [21]. Such explorations can enhance the operator's experience and heuristics that would otherwise never use the explored actions.…”

Section: Introductionmentioning

confidence: 99%

Real‐time transmission switching with neural networks

Bugaje

Cremer

Štrbac

2022

IET Generation Trans & Dist

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The classical formulation of the transmission switching problem as a mixed-integer problem is intractable for large systems in real-time control settings. Several heuristics have been proposed in the past to speed up the computation time, which only limits the number of switchable lines. In this paper, a real-time switching heuristic based on neural networks that provides almost instantaneous switching actions, are presented. The findings are shown on case studies of the IEEE 118-bus test system, and the results show that the proposed heuristic is robust to out of distribution data. Additionally, the proposed heuristic has significant computational savings while all other performance metrics like accuracy are similar to state-of-the-art machine learning methods proposed for transmission switching.

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Optimal Transmission Switching: Improving Exact Algorithms by Parallel Incumbent Solution Generation

Cited by 6 publications

References 42 publications

Minimization of Transmission Line Losses through Corrective Control of System Topology

Minimization of Transmission Line Losses through Corrective Control of System Topology

Optimum Parallel Processing Schemes to Improve the Computation Speed for Renewable Energy Allocation and Sizing Problems

Real‐time transmission switching with neural networks

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